What Inefficiencies Can BI Uncover in Your Call Center?

Inefficiency costs a call center time, as well as money. When agency operations and workflows aren’t running smoothly, the business might as well be a sieve. Time and money pour out every day, week, quarter and year.

Identifying the root cause behind the inefficiency used to be difficult, but that’s no longer the case. Business intelligence (BI) has forever changed the way organizations operate. They aren’t shooting into the dark hoping they’ll hit something; the lights are on, and they’ve seen the target. Through BI, they can fine-tune their aim until they hit the bull’s eye more often than not.

In the call center, the targets can be myriad. But most centers agree on a few top contenders, all of which can be uncovered by – and most importantly – solved with the deeper insights afforded by business intelligence.

Service Level Agreements (SLAs)

Every call center has standards, federally and internally mandated, which the center and its agents must meet. For example, one SLA is often the percentage of calls answered with a predetermined timeframe. When the percentage falls outsides established bounds, BI can identify if the cause is due to missed opportunities or ignored calls. Knowing the reason can help call centers refine their practices to get allowances back within the limits.

First Call Resolution (FCR)

The FCR rate is like a thermometer, indicating when a call center is in good or poor health. A high FCR rate indicates inefficiency is low, productivity is high, and customers are more-or-less completely satisfied.

When it’s low, it means something is amiss within the call center. While the FCR might not be able to pinpoint inefficiency directly, it can help pinpoint where the problem lies. A manager armed with BI tools and software, especially those that are native to the call center software itself, can follow the indicators and identify the root cause of the inefficiency. He or she then acts as a doctor would and tries out “treatment plans” to improve the center’s health.

Response Time

BI in the call center can also analyze response times, the average time taken to respond to a customer call. To decrease these times, call center managers can adjust different levers and monitor the impacts via call center software. As we all know, reducing response times leads to happier customers, and customer satisfaction is a key metric in the call center industry.

Abandonment Rate

Abandonment rates correlate with response times. If those times are long with no other recourse, such as a customer callback option, callers will hang up before they’re connected with an agent. Managers can reduce this rate by focusing on inefficiencies related to response times.

For example, if callers don’t even make it past your initial phone tree, perhaps you need to simplify the options. Or maybe callers seem to select one particular department an overwhelming majority of the time, causing wait times to be longer than normal and customers to hang up before their issue can be resolved. Having additional insights that BI analysis provides can uncover a wealth of details about why callers are abandoning their calls.

Average Handle Time

BI can identify when employees are struggling to perform their jobs because of inefficiencies in documentation and workflow, too. A number of studies have found that employees spend more time searching for information than using it to do their work or, in the case of a call canter, solving people’s problems.

If the inefficiency is due to inaccessible information, the solution is obvious: make the knowledge easier to access, from wherever, whenever and on whatever device they are. This will decrease the average amount of time spent on calls, including the administrative duties associated with them.

BI can also highlight where additional training might be needed to help resolve a customer issue. When it’s native to call center software, BI’s deeper analytics provide insights right down to the agent level on areas where skills improvement can help reduce hold times and better answer caller’s questions.

Customer Satisfaction

Customer feedback is essential to identifying inefficiencies, as well as opportunities for innovation. Their information can be combined with other metrics to develop an all-encompassing strategy and tactical plan that comes at call center inefficiency from multiple angles.

Quality Monitoring (QM) Scores

Efficiency is important, but so is effectiveness. When QM scores are low, managers should go to the data, i.e., recorded calls and accompanying information, to find out why and, more importantly, to solve for the problem. Making changes across the organization as a result of insights uncovered from BI analysis is key to raising QM scores companywide.

Break Times

Employees need breaks—in fact, they’re proven to help with efficiency and productivity—but excessive breaks are a problem. Call center BI can examine break times as a whole, as well as drill down to individual agents, to assess what’s causing the problem and identify potential solutions.

While the data can be used to enforce standards and codes of conduct, it can also serve other purposes. For instance, excessive breaks could reveal a lack of employee engagement. By addressing the underlying problem, the break-time issue could be mitigated altogether, with the added bonus of engaged employees who care about their behavior and the center’s success.

Inefficiency isn’t impossible to overcome when you have business intelligence in the call center. Taking a deeper dive into your data provides actionable intelligence that can be used to improve metrics like FCR and abandonment rates, as well as customer satisfaction and quality scores. And improving those is good news for everyone, from the agent answering calls to the manager proving ROI to the executive team.

Charlie Jergins started with TCN in 2012. Since then, he has held many positions including: Implementation Engineer, Consult & Retention, and most recently, our Business Analytics team. He now enjoys consulting with and developing value propositions such as revenue and expense projections as it relates to Business Intelligence.